polars.from_records#

polars.from_records( data: Sequence[Any], schema: SchemaDefinition | None = None, *, schema_overrides: SchemaDict | None = None, orient: Orientation | None = None, infer_schema_length: int | None = 100, ) → DataFrame[source]#

Construct a DataFrame from a sequence of sequences. This operation clones data.

Note that this is slower than creating from columnar memory.

Parameters:

dataSequence of sequences

Two-dimensional data represented as a sequence of sequences.

schemaSequence of str, (str,DataType) pairs, or a {str:DataType,} dict

The DataFrame schema may be declared in several ways:

As a dict of {name:type} pairs; if type is None, it will be auto-inferred.
As a list of column names; in this case types are automatically inferred.
As a list of (name,type) pairs; this is equivalent to the dictionary form.

If you supply a list of column names that does not match the names in the underlying data, the names given here will overwrite them. The number of names given in the schema should match the underlying data dimensions.

schema_overridesdict, default None

Support type specification or override of one or more columns; note that any dtypes inferred from the columns param will be overridden.

orient{None, ‘col’, ‘row’}

Whether to interpret two-dimensional data as columns or as rows. If None, the orientation is inferred by matching the columns and data dimensions. If this does not yield conclusive results, column orientation is used.

infer_schema_length

How many dictionaries/rows to scan to determine the data types if set to None all rows are scanned. This will be slow.

Returns:

DataFrame

Examples

>>> data = [[1, 2, 3], [4, 5, 6]]
>>> df = pl.from_records(data, schema=["a", "b"])
>>> df
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 4   │
│ 2   ┆ 5   │
│ 3   ┆ 6   │
└─────┴─────┘